PiFold：迈向有效且高效的蛋白质逆折叠
PiFold: Toward effective and efficient protein inverse folding
AI总结：
PiFold通过新颖的残基特征提取器和PiGNN层实现一次性蛋白质序列生成，在CATH 4.2上恢复率达51.66%，推理速度比自回归方法快70倍。
摘要：
我们如何有效且高效地设计能够折叠成所需结构的蛋白质序列？近年来，基于结构的蛋白质设计AI方法引起了越来越多的关注；然而，由于缺乏表达性特征和自回归序列解码器，很少有方法能够同时提高准确性和效率。为了解决这些问题，我们提出了PiFold，它包含一种新颖的残基特征提取器和PiGNN层，以一次性方式生成蛋白质序列，并提高了恢复率。实验表明，PiFold在CATH 4.2上可以达到51.66%的恢复率，而推理速度比自回归竞争对手快70倍。此外，PiFold在TS50和TS500上分别实现了58.72%和60.42%的恢复率分数。我们进行了全面的消融研究，以揭示不同类型蛋白质特征和模型设计的作用，从而启发进一步的简化和改进。PyTorch代码可在GitHub上获取。
英文摘要：
How can we design protein sequences folding into the desired structures effectively and efficiently? AI methods for structure-based protein design have attracted increasing attention in recent years; however, few methods can simultaneously improve the accuracy and efficiency due to the lack of expressive features and autoregressive sequence decoder. To address these issues, we propose PiFold, which contains a novel residue featurizer and PiGNN layers to generate protein sequences in a one-shot way with improved recovery. Experiments show that PiFold could achieve 51.66\% recovery on CATH 4.2, while the inference speed is 70 times faster than the autoregressive competitors. In addition, PiFold achieves 58.72\% and 60.42\% recovery scores on TS50 and TS500, respectively. We conduct comprehensive ablation studies to reveal the role of different types of protein features and model designs, inspiring further simplification and improvement. The PyTorch code is available at \href{https://github.com/A4Bio/PiFold}{GitHub}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
